{"data":{"event":{"id":"1fecb3a7-3259-4124-8457-bd352a240408","slug":"a-single-cell-rna-seq-catalog-of-ground-truth-gene-coregulation-73e92cfca3","title":"\n\nA single-cell RNA-seq catalog of ground truth gene coregulation \n\n","short_summary":"Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data representing known positive coregulation and B cells where isotypic mutual exclusion of {kappa} and {l","full_description":"Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data representing known positive coregulation and B cells where isotypic mutual exclusion of {kappa} and {lambda} light chains represent known negative coregulation. As an example, we use the catalog to evaluate seven normalization methods. Different normalization methods perform better for positive or negative coregulation, and results indicate implementation choices for advanced normalization methods The catalog may serve as a resource for evaluating novel proposed normalization methods and is available at https://huggingface.co/datasets/valsv/scrna-coregulation-benchmark","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-21T00:00:00.000Z","discovery_date":"2026-09-21T00:00:00.000Z","first_published_date":"2026-09-21T00:00:00.000Z","last_reviewed_date":"2026-09-21T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.1000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.001000","confidence_level":"low","score_explanation":"Auto-published from news ingest as a provisional placeholder. Score is conservative until a named release is identified and the record is rescored.","methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","original_methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","published_at":"2026-09-21T18:00:57.147Z","created_at":"2026-09-21T18:00:57.147Z","updated_at":"2026-09-21T18:00:57.147Z","flags":[],"domain_name":"Biology","domain_slug":"biology","methodology_version":"0.2"},"contributions":[{"id":"ae36cc1c-91d0-4767-a9c9-b640f2d03704","event_id":"1fecb3a7-3259-4124-8457-bd352a240408","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.1000","credit_share":"1.0000","contribution_score":"0.001000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-21T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-21T18:00:57.191Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"03746d67-41a0-42e5-8318-3233c4430389","event_id":"1fecb3a7-3259-4124-8457-bd352a240408","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.18.752692v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.18.752692v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-21T00:00:00.000Z","retrieved_at":"2026-09-21T18:00:57.231Z","title":"\n\nA single-cell RNA-seq catalog of ground truth gene coregulation \n\n","excerpt":"Single-cell RNA-sequencing measurements are uniquely well-poised to identify coregulated gene transcription. Coregulation should be apparent as correlation of expression levels, but at which unit to quantify expression for calculation of correlations is not clear. To enable evaluation of normalization methods for identification of coregulation we have curated and preprocessed 35 scRNA-seq samples with known regulature relationships into a catalog. These datasets contain promoter-reporter data re","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-21T18:00:57.231Z"}],"claims":[{"id":"98b0f9ef-1298-4c82-b090-b5af47103e96","event_id":"1fecb3a7-3259-4124-8457-bd352a240408","claim_text":"\n\nA single-cell RNA-seq catalog of ground truth gene coregulation \n\n","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-21T18:00:57.244Z","updated_at":"2026-09-21T18:00:57.244Z"}],"revisions":[{"id":"afa23cd5-3712-41a3-aa42-3362605214fa","event_id":"1fecb3a7-3259-4124-8457-bd352a240408","model_id":null,"previous_score":"0.000000","new_score":"0.001000","previous_factors":{},"new_factors":{"tier":1,"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-21T18:00:57.259Z"}],"secondary":[]},"methodology_version":"0.1"}